Project Summary This software development project combines the perspectives and software from three experienced groups to create and maintain what will be the most advanced and complete software package available for systems biology and the analysis of physiological data for research and clinical practice. The project brings together: (1) The JSim system for model based analysis using single or multi-scale models using ordinary and 1- dimensional partial differential equations for transport and metabolism (Butterworth and Bassingthwaighte, University of Washington), (2) the System Biology Workbench and related software coding packages invented by Herbert Sauro (Unversity of Washington), and (3) thermodynamically constrained and detailed equations for biochemical reactions created automatically from databases on reactions equilibria and kinetics (Daniel Beard, Medical College Wisconsin). The coalescence of these three powerful techniques, all of which utilize archival database markup languages like SBML and CellML, will foster model construction using JSim's inbuilt error identification and correction mechanisms. The toolkits and models will be disseminated worldwide in easily understood code, and will provide exactly and directly reproducible solutions on a wide range of computational platforms including Windows, Macintosh, and Linux. The models will be designed for experiment design and analysis, and the open source code for models and the analysis packages will be freely available to investigators everywhere. This powerful combination is relevant to the betterment of health as it provides investigators in genomics, molecular biology, physiology, and pharmacology with practical mechanisms for predicting and understanding integrated cellular organ and body function. Many of the current generation of biologists are not deeply trained in quantitative analysis of complex systems, so the availability of these tools will greatly facilitate their understanding of the biology and their ability to design more precisely targeted experimentation, more efficient data analysis, and improved therapies.